Method and device for providing offset model based calibration for analyte sensor

ABSTRACT

Methods and devices to detect analyte in body fluid are provided. Embodiments include processing sampled data from analyte sensor, determining a single, fixed, normal sensitivity value associated with the analyte sensor, estimating a windowed offset value associated with the analyte sensor for each available sampled data cluster, computing a time varying offset based on the estimated windowed offset value, and applying the time varying offset and the determined normal sensitivity value to the processed sampled data to estimate an analyte level for the sensor.

RELATED APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 13/550,515 filed Jul. 16, 2012, now U.S. Pat. No. 8,532,935,which is a continuation of U.S. patent application Ser. No. 12/362,479filed Jan. 29, 2009, now U.S. Pat. No. 8,224,415, entitled “Method andDevice for Providing Offset Model Based Calibration for Analyte Sensor”,the disclosures of each of which are incorporated herein by referencefor all purposes.

BACKGROUND

The detection of the level of glucose or other analytes, such aslactate, oxygen or the like, in certain individuals is vitally importantto their health. For example, the monitoring of glucose is particularlyimportant to individuals with diabetes. Diabetics may need to monitorglucose levels to determine when insulin is needed to reduce glucoselevels in their bodies or when additional glucose is needed to raise thelevel of glucose in their bodies.

Devices have been developed for continuous or automatic monitoring ofanalytes, such as glucose, in bodily fluid such as in the blood streamor in interstitial fluid. Some of these analyte measuring devices areconfigured so that at least a portion of the devices are positionedbelow a skin surface of a user, e.g., in a blood vessel or in thesubcutaneous tissue of a user.

Following the sensor insertion, the resulting potential trauma to theskin and/or underlying tissue, for example, by the sensor introducerand/or the sensor itself, may, at times, result in instability ofsignals monitored by the sensor. This may occur in a number of analytesensors, but not in all cases. This instability is characterized by adecrease in the sensor signal, and when this occurs, generally, theanalyte levels monitored may not be reported, recorded or output to theuser.

SUMMARY

Embodiments of the subject disclosure include device and methods ofdetermining early signal attenuation (ESA) in signals from analytesensors. More specifically, embodiments include method, device andsystem for processing sampled data from analyte sensor, determining asingle, fixed, normal sensitivity value associated with the analytesensor, estimating a windowed offset value associated with the analytesensor for each available sampled data cluster, computing a time varyingoffset based on the estimated windowed offset value, and applying thetime varying offset and the determined normal sensitivity value to theprocessed sampled data to estimate an analyte level for the sensor.

Also provided are systems, computer program products, and kits.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram of an embodiment of a data monitoring andmanagement system according to the present disclosure;

FIG. 2 shows a block diagram of an embodiment of the transmitter unit ofthe data monitoring and management system of FIG. 1;

FIG. 3 shows a block diagram of an embodiment of the receiver/monitorunit of the data monitoring and management system of FIG. 1;

FIG. 4 shows a schematic diagram of an embodiment of an analyte sensoraccording to the present disclosure;

FIGS. 5A-5B show a perspective view and a cross sectional view,respectively of an embodiment the analyte sensor of FIG. 4;

FIG. 6 is a flowchart illustrating the offset model based analyte sensordata calibration in accordance with one aspect of the presentdisclosure; and

FIG. 7 is a flowchart illustrating the normal sensitivity determinationroutine of FIG. 6 associated with the analyte sensor in accordance withone embodiment of the present disclosure.

DETAILED DESCRIPTION

Before the present disclosure is described in additional detail, it isto be understood that this disclosure is not limited to particularembodiments described, as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting, since the scope of the present disclosure will be limited onlyby the appended claims.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range, is encompassed within the disclosure. That the upper andlower limits of these smaller ranges may independently be included inthe smaller ranges is also encompassed within the disclosure, subject toany specifically excluded limit in the stated range. Where the statedrange includes one or both of the limits, ranges excluding either orboth of those included limits are also included in the disclosure.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this disclosure belongs. Although any methods andmaterials similar or equivalent to those described herein can also beused in the practice or testing of the present disclosure, the preferredmethods and materials are now described. All publications mentionedherein are incorporated herein by reference to disclose and describe themethods and/or materials in connection with which the publications arecited.

It must be noted that as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include plural referents unless thecontext clearly dictates otherwise.

The publications discussed herein are provided solely for theirdisclosure prior to the filing date of the present application. Nothingherein is to be construed as an admission that the present disclosure isnot entitled to antedate such publication by virtue of prior disclosure.Further, the dates of publication provided may be different from theactual publication dates which may need to be independently confirmed.

As will be apparent to those of skill in the art upon reading thisdisclosure, each of the individual embodiments described and illustratedherein has discrete components and features which may be readilyseparated from or combined with the features of any of the other severalembodiments without departing from the scope or spirit of the presentdisclosure.

The figures shown herein are not necessarily drawn to scale, with somecomponents and features being exaggerated for clarity.

Generally, embodiments of the present disclosure relate to methods anddevices for detecting at least one analyte such as glucose in bodyfluid. In certain embodiments, the present disclosure relates to thecontinuous and/or automatic in vivo monitoring of the level of ananalyte using an analyte sensor.

Accordingly, embodiments include analyte monitoring devices and systemsthat include an analyte sensor—at least a portion of which ispositionable beneath the skin of the user—for the in vivo detection, ofan analyte, such as glucose, lactate, and the like, in a body fluid.Embodiments include wholly implantable analyte sensors and analytesensors in which only a portion of the sensor is positioned under theskin and a portion of the sensor resides above the skin, e.g., forcontact to a transmitter, receiver, transceiver, processor, etc. Thesensor may be, for example, subcutaneously positionable in a patient forthe continuous or periodic monitoring of a level of an analyte in apatient's interstitial fluid. For the purposes of this description,continuous monitoring and periodic monitoring will be usedinterchangeably, unless noted otherwise. The analyte level may becorrelated and/or converted to analyte levels in blood or other fluids.In certain embodiments, an analyte sensor may be positioned in contactwith interstitial fluid to detect the level of glucose, which detectedglucose may be used to infer the glucose level in the patient'sbloodstream. Analyte sensors may be insertable into a vein, artery, orother portion of the body containing fluid. Embodiments of the analytesensors of the subject disclosure may be configured for monitoring thelevel of the analyte over a time period which may range from minutes,hours, days, weeks, or longer.

Of interest are analyte sensors, such as glucose sensors, that arecapable of in vivo detection of an analyte for about one hour or more,e.g., about a few hours or more, e.g., about a few days or more, e.g.,about three or more days, e.g., about five days or more, e.g., aboutseven days or more, e.g., about several weeks or at least one month.Future analyte levels may be predicted based on information obtained,e.g., the current analyte level at time t₀, the rate of change of theanalyte, etc. Predictive alarms may notify the user of predicted analytelevels that may be of concern prior in advance of the analyte levelreaching the future level. This enables the user an opportunity to takecorrective action.

FIG. 1 shows a data monitoring and management system such as, forexample, an analyte (e.g., glucose) monitoring system 100 in accordancewith certain embodiments. Embodiments of the subject disclosure arefurther described primarily with respect to glucose monitoring devicesand systems, and methods of glucose detection, for convenience only andsuch description is in no way intended to limit the scope of thedisclosure. It is to be understood that the analyte monitoring systemmay be configured to monitor a variety of analytes at the same time orat different times.

Analytes that may be monitored include, but are not limited to, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin,creatine kinase (e.g., CK-MB), creatine, DNA, fructosamine, glucose,glutamine, growth hormones, hormones, ketones, lactate, peroxide,prostate-specific antigen, prothrombin, RNA, thyroid stimulatinghormone, and troponin. The concentration of drugs, such as, for example,antibiotics (e.g., gentamicin, vancomycin, and the like), digitoxin,digoxin, drugs of abuse, theophylline, and warfarin, may also bemonitored. In those embodiments that monitor more than one analyte, theanalytes may be monitored at the same or different times.

The analyte monitoring system 100 includes a sensor 101, a dataprocessing unit 102 connectable to the sensor 101, and a primaryreceiver unit 104 which is configured to communicate with the dataprocessing unit 102 via a communication link 103. In certainembodiments, the primary receiver unit 104 may be further configured totransmit data to a data processing terminal 105 to evaluate or otherwiseprocess or format data received by the primary receiver unit 104. Thedata processing terminal 105 may be configured to receive data directlyfrom the data processing unit 102 via a communication link which mayoptionally be configured for bi-directional communication. Further, thedata processing unit 102 may include a transmitter or a transceiver totransmit and/or receive data to and/or from the primary receiver unit104, the data processing terminal 105 or optionally the secondaryreceiver unit 106.

Also shown in FIG. 1 is an optional secondary receiver unit 106 which isoperatively coupled to the communication link and configured to receivedata transmitted from the data processing unit 102. The secondaryreceiver unit 106 may be configured to communicate with the primaryreceiver unit 104, as well as the data processing terminal 105. Thesecondary receiver unit 106 may be configured for bi-directionalwireless communication with each of the primary receiver unit 104 andthe data processing terminal 105. As discussed in further detail below,in certain embodiments the secondary receiver unit 106 may be ade-featured receiver as compared to the primary receiver, i.e., thesecondary receiver may include a limited or minimal number of functionsand features as compared with the primary receiver unit 104. As such,the secondary receiver unit 106 may include a smaller (in one or more,including all, dimensions), compact housing or embodied in a device suchas a wrist watch, arm band, etc., for example. Alternatively, thesecondary receiver unit 106 may be configured with the same orsubstantially similar functions and features as the primary receiverunit 104. The secondary receiver unit 106 may include a docking portionto be mated with a docking cradle unit for placement by, e.g., thebedside for night time monitoring, and/or bi-directional communication.

Only one sensor 101, data processing unit 102 and data processingterminal 105 are shown in the embodiment of the analyte monitoringsystem 100 illustrated in FIG. 1. However, it will be appreciated by oneof ordinary skill in the art that the analyte monitoring system 100 mayinclude more than one sensor 101 and/or more than one data processingunit 102, and/or more than one data processing terminal 105. Multiplesensors may be positioned in a patient for analyte monitoring at thesame or different times. In certain embodiments, analyte informationobtained by a first positioned sensor may be employed as a comparison toanalyte information obtained by a second sensor. This may be useful toconfirm or validate analyte information obtained from one or both of thesensors. Such redundancy may be useful if analyte information iscontemplated in critical therapy-related decisions. In certainembodiments, a first sensor may be used to calibrate a second sensor.

The analyte monitoring system 100 may be a continuous monitoring system,or semi-continuous, or a discrete monitoring system. In amulti-component environment, each component may be configured to beuniquely identified by one or more of the other components in the systemso that communication conflict may be readily resolved between thevarious components within the analyte monitoring system 100. Forexample, unique IDs, communication channels, and the like, may be used.

In certain embodiments, the sensor 101 is physically positioned in or onthe body of a user whose analyte level is being monitored. The sensor101 may be configured to at least periodically sample the analyte levelof the user and convert the sampled analyte level into a correspondingsignal for transmission by the data processing unit 102. The dataprocessing unit 102 is coupleable to the sensor 101 so that both devicesare positioned in or on the user's body, with at least a portion of theanalyte sensor 101 positioned transcutaneously. The data processing unit102 performs data processing functions, where such functions may includebut are not limited to, filtering and encoding of data signals, each ofwhich corresponds to a sampled analyte level of the user, fortransmission to the primary receiver unit 104 via the communication link103. In one embodiment, the sensor 101 or the data processing unit 102or a combined sensor/data processing unit may be wholly implantableunder the skin layer of the user.

In one aspect, the primary receiver unit 104 may include an analoginterface section including a radio frequency (RF) receiver and anantenna that is configured to communicate with the data processing unit102 via the communication link 103, a data processing section forprocessing the received data from the data processing unit 102 such asdata decoding, error detection and correction, data clock generation,and/or data bit recovery.

In operation, the primary receiver unit 104 in certain embodiments isconfigured to synchronize with the data processing unit 102 to uniquelyidentify the data processing unit 102, based on, for example, anidentification information of the data processing unit 102, andthereafter, to periodically receive signals transmitted from the dataprocessing unit 102 associated with the monitored analyte levelsdetected by the sensor 101.

Referring again to FIG. 1, the data processing terminal 105 may includea personal computer, a portable computer such as a laptop or a handhelddevice (e.g., personal digital assistants (PDAs), telephone such as acellular phone (e.g., a multimedia and Internet-enabled mobile phonesuch as an iPhone or similar phone), mp3 player, pager, and the like),drug delivery device, each of which may be configured for datacommunication with the receiver via a wired or a wireless connection.Additionally, the data processing terminal 105 may further be connectedto a data network (not shown) for storing, retrieving, updating, and/oranalyzing data corresponding to the detected analyte level of the user.

The data processing terminal 105 may include an infusion device such asan insulin infusion pump or the like, which may be configured toadminister insulin to patients, and which may be configured tocommunicate with the primary receiver unit 104 for receiving, amongothers, the measured analyte level. Alternatively, the primary receiverunit 104 may be configured to integrate an infusion device therein sothat the primary receiver unit 104 is configured to administer insulin(or other appropriate drug) therapy to patients, for example, foradministering and modifying basal profiles, as well as for determiningappropriate boluses for administration based on, among others, thedetected analyte levels received from the data processing unit 102. Aninfusion device may be an external device or an internal device (whollyimplantable in a user).

In particular embodiments, the data processing terminal 105, which mayinclude an insulin pump, may be configured to receive the analytesignals from the data processing unit 102, and thus, incorporate thefunctions of the primary receiver unit 104 including data processing formanaging the patient's insulin therapy and analyte monitoring. Incertain embodiments, the communication link 103 as well as one or moreof the other communication interfaces shown in FIG. 1 may use one ormore of an RF communication protocol, an infrared communicationprotocol, a Bluetooth® enabled communication protocol, an 802.11xwireless communication protocol, or an equivalent wireless communicationprotocol which would allow secure, wireless communication of severalunits (for example, per HIPAA requirements) while avoiding potentialdata collision and interference.

FIG. 2 is a block diagram of the data processing unit of the datamonitoring and detection system shown in FIG. 1 in accordance withcertain embodiments. The data processing unit 102 thus may include oneor more of an analog interface 201 configured to communicate with thesensor 101 (FIG. 1), a user input 202, and a temperature detectionsection 203, each of which is operatively coupled to a transmitterprocessor 204 such as a central processing unit (CPU). The transmittermay include user input and/or interface components or may be free ofuser input and/or interface components.

Further shown in FIG. 2 are serial communication section 205 and an RFtransmitter 206, each of which is also operatively coupled to thetransmitter processor 204. Moreover, a power supply 207, such as abattery, may also be provided in the data processing unit 102 to providethe necessary power for the data processing unit 102. Additionally, ascan be seen from the Figure, clock 208 may be provided to, among others,supply real time information to the transmitter processor 204.

As can be seen in the embodiment of FIG. 2, the sensor unit 101 (FIG. 1)includes four contacts, three of which are electrodes—work electrode (W)210, guard contact (G) 211, reference electrode (R) 212, and counterelectrode (C) 213, each operatively coupled to the analog interface 201of the data processing unit 102. Analog interface 201 is further coupledto serial communication section 205 via coupling connection 209. Incertain embodiments, each of the work electrode (W) 210, guard contact(G) 211, reference electrode (R) 212, and counter electrode (C) 213 maybe made using a conductive material that may be applied by, e.g.,chemical vapor deposition (CVD), physical vapor deposition, sputtering,reactive sputtering, printing, coating, ablating (e.g., laser ablation),painting, dip coating, etching, and the like. Materials include but arenot limited to aluminum, carbon (such as graphite), cobalt, copper,gallium, gold, indium, iridium, iron, lead, magnesium, mercury (as anamalgam), nickel, niobium, osmium, palladium, platinum, rhenium,rhodium, selenium, silicon (e.g., doped polycrystalline silicon),silver, tantalum, tin, titanium, tungsten, uranium, vanadium, zinc,zirconium, mixtures thereof, and alloys, oxides, or metallic compoundsof these elements.

The processor 204 may be configured to generate and/or process controlsignals to the various sections of the data processing unit 102 duringthe operation of the data processing unit 102. In certain embodiments,the processor 204 also includes memory (not shown) for storing data suchas the identification information for the data processing unit 102, aswell as the data associated with signals received from the sensor 101.The stored information may be retrieved and processed for transmissionto the primary receiver unit 104 under the control of the processor 204.Furthermore, the power supply 207 may include a commercially availablebattery.

In certain embodiments, a manufacturing process of the data processingunit 102 may place the data processing unit 102 in the lower power,non-operating state (i.e., post-manufacture sleep mode). In this manner,the shelf life of the data processing unit 102 may be significantlyimproved. Moreover, as shown in FIG. 2, while the power supply unit 207is shown as coupled to the processor 204, and as such, the processor 204is configured to provide control of the power supply unit 207, it shouldbe noted that within the scope of the present disclosure, the powersupply unit 207 is configured to provide the necessary power to each ofthe components of the data processing unit 102 shown in FIG. 2.

Referring back to FIG. 2, the power supply section 207 of the dataprocessing unit 102 in one embodiment may include a rechargeable batteryunit that may be recharged by a separate power supply recharging unit(for example, provided in the receiver unit 104) so that the dataprocessing unit 102 may be powered for a longer period of usage time. Incertain embodiments, the data processing unit 102 may be configuredwithout a battery in the power supply section 207, in which case thedata processing unit 102 may be configured to receive power from anexternal power supply source (for example, a battery, electrical outlet,etc.) as discussed in further detail below.

Referring yet again to FIG. 2, a temperature detection section 203 ofthe data processing unit 102 is configured to monitor the temperature ofthe skin near the sensor insertion site. The temperature reading may beused to adjust the analyte readings obtained from the analog interface201. Also shown is a leak detection circuit 214 coupled to the guardtrace (G) 211 and the processor 204 in the data processing unit 102 ofthe data monitoring and management system 100. The leak detectioncircuit 214 may be configured to detect leakage current in the sensor101 to determine whether the measured sensor data is corrupt or whetherthe measured data from the sensor 101 is accurate. Such detection maytrigger a notification to the user.

FIG. 3 is a block diagram of the receiver/monitor unit such as theprimary receiver unit 104 of the data monitoring and management systemshown in FIG. 1 in accordance with certain embodiments. The primaryreceiver unit 104 includes one or more of: a blood glucose test stripinterface 301, an RF receiver 302, an input 303, a temperature detectionsection 304, and a clock 305, each of which is operatively coupled to aprocessing and storage section 307. The primary receiver unit 104 alsoincludes a power supply 306 operatively coupled to a power conversionand monitoring section 308. Further, the power conversion and monitoringsection 308 is also coupled to the receiver processor 307. Moreover,also shown are a receiver serial communication section 309, and anoutput 310, each operatively coupled to the processing and storage unit307. The receiver may include user input and/or interface components ormay be free of user input and/or interface components.

In certain embodiments, the test strip interface 301 includes a glucoselevel testing portion to receive a blood (or other body fluid sample)glucose test or information related thereto. For example, the interfacemay include a test strip port to receive a glucose test strip. Thedevice may determine the glucose level of the test strip, and optionallydisplay (or otherwise notice) the glucose level on the output 310 of theprimary receiver unit 104. Any suitable test strip may be employed,e.g., test strips that only require a very small amount (e.g., onemicroliter or less, e.g., 0.5 microliter or less, e.g., 0.1 microliteror less), of applied sample to the strip in order to obtain accurateglucose information, e.g. FreeStyle® blood glucose test strips fromAbbott Diabetes Care, Inc. Glucose information obtained by the in vitroglucose testing device may be used for a variety of purposes,computations, etc. For example, the information may be used to calibratesensor 101, confirm results of the sensor 101 to increase the confidencethereof (e.g., in instances in which information obtained by sensor 101is employed in therapy related decisions), etc.

In one aspect, the RF receiver 302 is configured to communicate, via thecommunication link 103 (FIG. 1) with the RF transmitter 206 of the dataprocessing unit 102, to receive encoded data from the data processingunit 102 for, among others, signal mixing, demodulation, and other dataprocessing. The input 303 of the primary receiver unit 104 is configuredto allow the user to enter information into the primary receiver unit104 as needed. In one aspect, the input 303 may include keys of akeypad, a touch-sensitive screen, and/or a voice-activated input commandunit, and the like. The temperature monitor section 304 may beconfigured to provide temperature information of the primary receiverunit 104 to the processing and control section 307, while the clock 305provides, among others, real time or clock information to the processingand storage section 307.

Each of the various components of the primary receiver unit 104 shown inFIG. 3 is powered by the power supply 306 (or other power supply) which,in certain embodiments, includes a battery. Furthermore, the powerconversion and monitoring section 308 is configured to monitor the powerusage by the various components in the primary receiver unit 104 foreffective power management and may alert the user, for example, in theevent of power usage which renders the primary receiver unit 104 insub-optimal operating conditions. The serial communication section 309in the primary receiver unit 104 is configured to provide abi-directional communication path from the testing and/or manufacturingequipment for, among others, initialization, testing, and configurationof the primary receiver unit 104. Serial communication section 309 canalso be used to upload data to a computer, such as time-stamped bloodglucose data. The communication link with an external device (not shown)can be made, for example, by cable (such as USB or serial cable),infrared (IR) or RF link. The output/display 310 of the primary receiverunit 104 is configured to provide, among others, a graphical userinterface (GUI), and may include a liquid crystal display (LCD) fordisplaying information. Additionally, the output/display 310 may alsoinclude an integrated speaker for outputting audible signals as well asto provide vibration output as commonly found in handheld electronicdevices, such as mobile telephones, pagers, etc. In certain embodiments,the primary receiver unit 104 also includes an electro-luminescent lampconfigured to provide backlighting to the output 310 for output visualdisplay in dark ambient surroundings.

Referring back to FIG. 3, the primary receiver unit 104 may also includea storage section such as a programmable, non-volatile memory device aspart of the processor 307, or provided separately in the primaryreceiver unit 104, operatively coupled to the processor 307. Theprocessor 307 may be configured to perform Manchester decoding (or otherprotocol(s)) as well as error detection and correction upon the encodeddata received from the data processing unit 102 via the communicationlink 103.

In further embodiments, the data processing unit 102 and/or the primaryreceiver unit 104 and/or the secondary receiver unit 106, and/or thedata processing terminal/infusion section 105 may be configured toreceive the blood glucose value wirelessly over a communication linkfrom, for example, a blood glucose meter. In further embodiments, a usermanipulating or using the analyte monitoring system 100 (FIG. 1) maymanually input the blood glucose value using, for example, a userinterface (for example, a keyboard, keypad, voice commands, and thelike) incorporated in the one or more of the data processing unit 102,the primary receiver unit 104, secondary receiver unit 106, or the dataprocessing terminal/infusion section 105.

Additional detailed descriptions of embodiments of the continuousanalyte monitoring system, embodiments of its various components areprovided in U.S. Pat. No. 6,175,752 issued Jan. 16, 2001 entitled“Analyte Monitoring Device and Methods of Use”, and in application Ser.No. 10/745,878 filed Dec. 26, 2003, now U.S. Pat. No. 7,811,231,entitled “Continuous Glucose Monitoring System and Methods of Use”, eachassigned to the Assignee of the present application, and the disclosuresof each of which are incorporated herein by reference for all purposes.

FIG. 4 schematically shows an embodiment of an analyte sensor inaccordance with the present disclosure. The sensor 400 includeselectrodes 401, 402 and 403 on a base 404. The sensor may be whollyimplantable in a user or may be configured so that only a portion ispositioned within (internal) a user and another portion outside(external) a user. For example, the sensor 400 may include a portionpositionable above a surface of the skin 410, and a portion positionedbelow the skin. In such embodiments, the external portion may includecontacts (connected to respective electrodes of the second portion bytraces) to connect to another device also external to the user such as atransmitter unit. While the embodiment of FIG. 4 shows three electrodesside-by-side on the same surface of base 404, other configurations arecontemplated, e.g., fewer or greater electrodes, some or all electrodeson different surfaces of the base or present on another base, some orall electrodes stacked together, electrodes of differing materials anddimensions, etc.

FIG. 5A shows a perspective view of an embodiment of an electrochemicalanalyte sensor 500 having a first portion (which in this embodiment maybe characterized as a major portion) positionable above a surface of theskin 510, and a second portion (which in this embodiment may becharacterized as a minor portion) that includes an insertion tip 530positionable below the skin, e.g., penetrating through the skin andinto, e.g., the subcutaneous space 520, in contact with the user'sbiofluid such as interstitial fluid. Contact portions of a workingelectrode 501, a reference electrode 502, and a counter electrode 503are positioned on the portion of the sensor 500 situated above the skinsurface 510. Working electrode 501, a reference electrode 502, and acounter electrode 503 are shown at the second section and particularlyat the insertion tip 530. Traces may be provided from the electrode atthe tip to the contact, as shown in FIG. 5A. It is to be understood thatgreater or fewer electrodes may be provided on a sensor. For example, asensor may include more than one working electrode and/or the counterand reference electrodes may be a single counter/reference electrode,etc.

FIG. 5B shows a cross sectional view of a portion of the sensor 500 ofFIG. 5A. The electrodes 501, 502 and 503, of the sensor 500 as well asthe substrate and the dielectric layers are provided in a layeredconfiguration or construction. For example, as shown in FIG. 5B, in oneaspect, the sensor 500 (such as the sensor 101 FIG. 1), includes asubstrate layer 504, and a first conducting layer 501 such as carbon,gold, etc., disposed on at least a portion of the substrate layer 504,and which may provide the working electrode. Also shown disposed on atleast a portion of the first conducting layer 501 is a sensing layer508.

Referring back to FIG. 5B, a first insulation layer such as a firstdielectric layer 505 is disposed or layered on at least a portion of thefirst conducting layer 501, and further, a second conducting layer 509may be disposed or stacked on top of at least a portion of the firstinsulation layer (or dielectric layer) 505. As shown in FIG. 5B, thesecond conducting layer 509 may provide the reference electrode 502, andin one aspect, may include a layer of silver/silver chloride (Ag/AgCl),gold, etc.

Referring still again to FIG. 5B, a second insulation layer 506 such asa dielectric layer in one embodiment may be disposed or layered on atleast a portion of the second conducting layer 509. Further, a thirdconducting layer 503 may provide the counter electrode 503. It may bedisposed on at least a portion of the second insulation layer 506.Finally, a third insulation layer 507 may be disposed or layered on atleast a portion of the third conducting layer 503. In this manner, thesensor 500 may be layered such that at least a portion of each of theconducting layers is separated by a respective insulation layer (forexample, a dielectric layer).

The embodiment of FIGS. 5A and 5B show the layers having differentlengths. Some or all of the layers may have the same or differentlengths and/or widths.

In certain embodiments, some or all of the electrodes 501, 502, 503 maybe provided on the same side of the substrate 504 in the layeredconstruction as described above, or alternatively, may be provided in aco-planar manner such that two or more electrodes may be positioned onthe same plane (e.g., side-by side (e.g., parallel) or angled relativeto each other) on the substrate 504. For example, co-planar electrodesmay include a suitable spacing there between and/or include dielectricmaterial or insulation material disposed between the conductinglayers/electrodes. Furthermore, in certain embodiments one or more ofthe electrodes 501, 502, 503 may be disposed on opposing sides of thesubstrate 504. In such embodiments, contact pads may be on the same ordifferent sides of the substrate. For example, an electrode may be on afirst side and its respective contact may be on a second side, e.g., atrace connecting the electrode and the contact may traverse through thesubstrate.

In certain embodiments, the data processing unit 102 may be configuredto perform sensor insertion detection and data quality analysis,information pertaining to which may also be transmitted to the primaryreceiver unit 104 periodically at the predetermined time interval. Inturn, the receiver unit 104 may be configured to perform, for example,skin temperature compensation/correction as well as calibration of thesensor data received from the data processing unit 102.

As noted above, analyte sensors may include an analyte-responsive enzymein a sensing layer. Some analytes, such as oxygen, can be directlyelectrooxidized or electroreduced on a sensor, and more specifically atleast on a working electrode of a sensor. Other analytes, such asglucose and lactate, require the presence of at least one electrontransfer agent and/or at least one catalyst to facilitate theelectrooxidation or electroreduction of the analyte. Catalysts may alsobe used for those analytes, such as oxygen, that can be directlyelectrooxidized or electroreduced on the working electrode. For theseanalytes, each working electrode includes a sensing layer (see forexample sensing layer 508 of FIG. 5B) formed proximate to or on asurface of a working electrode. In many embodiments, a sensing layer isformed near or on only a small portion of at least a working electrode.

A variety of different sensing layer configurations may be used. Incertain embodiments, the sensing layer is deposited on the conductivematerial of a working electrode. The sensing layer may extend beyond theconductive material of the working electrode. In some cases, the sensinglayer may also extend over other electrodes, e.g., over the counterelectrode and/or reference electrode (or counter/reference is provided).The sensing layer may be integral with the material of an electrode.

A sensing layer that is in direct contact with the working electrode maycontain an electron transfer agent to transfer electrons directly orindirectly between the analyte and the working electrode, and/or acatalyst to facilitate a reaction of the analyte.

A sensing layer that is not in direct contact with the working electrodemay include a catalyst that facilitates a reaction of the analyte.However, such sensing layers may not include an electron transfer agentthat transfers electrons directly from the working electrode to theanalyte, as the sensing layer is spaced apart from the workingelectrode. One example of this type of sensor is a glucose or lactatesensor which includes an enzyme (e.g., glucose oxidase, glucosedehydrogenase, lactate oxidase, and the like) in the sensing layer. Theglucose or lactate may react with a second compound in the presence ofthe enzyme. The second compound may then be electrooxidized orelectroreduced at the electrode. Changes in the signal at the electrodeindicate changes in the level of the second compound in the fluid andare proportional to changes in glucose or lactate level and, thus,correlate to the analyte level.

In certain embodiments which include more than one working electrode,one or more of the working electrodes do not have a correspondingsensing layer, or have a sensing layer which does not contain one ormore components (e.g., an electron transfer agent and/or catalyst)needed to electrolyze the analyte. Thus, the signal at this workingelectrode corresponds to background signal which may be removed from theanalyte signal obtained from one or more other working electrodes thatare associated with fully-functional sensing layers by, for example,subtracting the signal.

In certain embodiments, the sensing layer includes one or more electrontransfer agents. Electron transfer agents that may be employed areelectroreducible and electrooxidizable ions or molecules having redoxpotentials that are a few hundred millivolts above or below the redoxpotential of the standard calomel electrode (SCE). The electron transferagent may be organic, organometallic, or inorganic.

In certain embodiments, electron transfer agents have structures orcharges which prevent or substantially reduce the diffusional loss ofthe electron transfer agent during the period of time that the sample isbeing analyzed. For example, electron transfer agents include but arenot limited to a redox species, e.g., bound to a polymer which can inturn be disposed on or near the working electrode. The bond between theredox species and the polymer may be covalent, coordinative, or ionic.Although any organic or organometallic redox species may be bound to apolymer and used as an electron transfer agent, in certain embodimentsthe redox species is a transition metal compound or complex, e.g.,osmium, ruthenium, iron, and cobalt compounds or complexes. It will berecognized that many redox species described for use with a polymericcomponent may also be used, without a polymeric component.

One type of polymeric electron transfer agent contains a redox speciescovalently bound in a polymeric composition. An example of this type ofmediator is poly(vinylferrocene). Another type of electron transferagent contains an ionically-bound redox species. This type of mediatormay include a charged polymer coupled to an oppositely charged redoxspecies. Examples of this type of mediator include a negatively chargedpolymer coupled to a positively charged redox species such as an osmiumor ruthenium polypyridyl cation. Another example of an ionically-boundmediator is a positively charged polymer such as quaternizedpoly(4-vinyl pyridine) or poly(1-vinyl imidazole) coupled to anegatively charged redox species such as ferricyanide or ferrocyanide.In other embodiments, electron transfer agents include a redox speciescoordinatively bound to a polymer. For example, the mediator may beformed by coordination of an osmium or cobalt 2,2′-bipyridyl complex topoly(1-vinyl imidazole) or poly(4-vinyl pyridine).

Suitable electron transfer agents are osmium transition metal complexeswith one or more ligands, each ligand having a nitrogen-containingheterocycle such as 2,2′-bipyridine, 1,10-phenanthroline, or derivativesthereof. The electron transfer agents may also have one or more ligandscovalently bound in a polymer, each ligand having at least onenitrogen-containing heterocycle, such as pyridine, imidazole, orderivatives thereof. The present disclosure may employ electron transferagents having a redox potential ranging from about −100 mV to about +150mV versus the standard calomel electrode (SCE), e.g., ranges from about−100 mV to about +150 mV, e.g., ranges from about −50 mV to about +50mV, e.g., electron transfer agents have osmium redox centers and a redoxpotential ranging from +50 mV to −150 mV versus SCE.

The sensing layer may also include a catalyst which is capable ofcatalyzing a reaction of the analyte. The catalyst may also, in someembodiments, act as an electron transfer agent. One example of asuitable catalyst is an enzyme which catalyzes a reaction of theanalyte. For example, a catalyst, such as a glucose oxidase, glucosedehydrogenase (e.g., pyrroloquinoline quinone glucose dehydrogenase(PQQ)), or oligosaccharide dehydrogenase), may be used when the analyteof interest is glucose. A lactate oxidase or lactate dehydrogenase maybe used when the analyte of interest is lactate. Laccase may be usedwhen the analyte of interest is oxygen or when oxygen is generated orconsumed in response to a reaction of the analyte.

In certain embodiments, a catalyst may be attached to a polymer, crosslinking the catalyst with another electron transfer agent (which, asdescribed above, may be polymeric). A second catalyst may also be usedin certain embodiments. This second catalyst may be used to catalyze areaction of a product compound resulting from the catalyzed reaction ofthe analyte. The second catalyst may operate with an electron transferagent to electrolyze the product compound to generate a signal at theworking electrode. Alternatively, a second catalyst may be provided inan interferent-eliminating layer to catalyze reactions that removeinterferents.

Certain embodiments include a Wired Enzyme™ sensing layer that works ata gentle oxidizing potential, e.g., a potential of about +40 mV. Thissensing layer uses an osmium (Os)-based mediator designed for lowpotential operation and is stably anchored in a polymeric layer.Accordingly, in certain embodiments the sensing element is a redoxactive component that includes (1) Osmium-based mediator moleculesattached by stable (bidente) ligands anchored to a polymeric backbone,and (2) glucose oxidase enzyme molecules. These two constituents arecrosslinked together.

A mass transport limiting layer (not shown), e.g., an analyte fluxmodulating layer, may be included with the sensor to act as adiffusion-limiting barrier to reduce the rate of mass transport of theanalyte, for example, glucose or lactate, into the region around theworking electrodes. The mass transport limiting layers are useful inlimiting the flux of an analyte to a working electrode in anelectrochemical sensor so that the sensor is linearly responsive over alarge range of analyte concentrations and is easily calibrated. Masstransport limiting layers may include polymers and may be biocompatible.A mass transport limiting layer may serve many functions, e.g.,functionalities of a biocompatible layer and/or interferent-eliminatinglayer may be provided by the mass transport limiting layer.

In certain embodiments, a mass transport limiting layer is a membranecomposed of crosslinked polymers containing heterocyclic nitrogengroups, such as polymers of polyvinylpyridine and polyvinylimidazole.Electrochemical sensors equipped with such membranes have considerablesensitivity and stability, and a large signal-to-noise ratio, in avariety of conditions.

According to certain embodiments, a membrane is formed by crosslinkingin situ a polymer, modified with a zwitterionic moiety, a non-pyridinecopolymer component, and optionally another moiety that is eitherhydrophilic or hydrophobic, and/or has other desirable properties, in analcohol-buffer solution. The modified polymer may be made from aprecursor polymer containing heterocyclic nitrogen groups. Optionally,hydrophilic or hydrophobic modifiers may be used to “fine-tune” thepermeability of the resulting membrane to an analyte of interest.Optional hydrophilic modifiers, such as poly(ethylene glycol), hydroxylor polyhydroxyl modifiers, may be used to enhance the biocompatibilityof the polymer or the resulting membrane.

A biocompatible layer (not shown) may be provided over at least thatportion of the sensor which is subcutaneously inserted into the patient.The biocompatible layer may be incorporated in theinterferent-eliminating layer or in the mass transport limiting layer ormay be a separate layer. The layer may prevent the penetration of largebiomolecules into the electrodes. The biocompatible layer may alsoprevent protein adhesion to the sensor, formation of blood clots, andother undesirable interactions between the sensor and body. For example,a sensor may be completely or partially covered on its exterior with abiocompatible coating.

An interferent-eliminating layer (not shown) may be included in thesensor. The interferent-eliminating layer may be incorporated in thebiocompatible layer or in the mass transport limiting layer or may be aseparate layer. Interferents are molecules or other species that areelectroreduced or electrooxidized at the electrode, either directly orvia an electron transfer agent, to produce a false signal. In oneembodiment, a film or membrane prevents the penetration of one or moreinterferents into the region around the working electrode. In manyembodiments, this type of interferent-eliminating layer is much lesspermeable to one or more of the interferents than to the analyte. Aninterferent-eliminating layer may include ionic components to reduce thepermeability of the interferent-eliminating layer to ionic interferentshaving the same charge as the ionic components. Another example of aninterferent-eliminating layer includes a catalyst for catalyzing areaction which removes interferents.

A sensor may also include an active agent such as an anticlotting and/orantiglycolytic agent(s) disposed on at least a portion a sensor that ispositioned in a user. An anticlotting agent may reduce or eliminate theclotting of blood or other body fluid around the sensor, particularlyafter insertion of the sensor. Blood clots may foul the sensor orirreproducibly reduce the amount of analyte which diffuses into thesensor. Examples of useful anticlotting agents include heparin andtissue plasminogen activator (TPA), as well as other known anticlottingagents. Embodiments may include an antiglycolytic agent or precursorthereof. The term “antiglycolytic” is used broadly herein to include anysubstance that at least retards glucose consumption of living cells.

Sensors described herein may be configured to require no systemcalibration or no user calibration. For example, a sensor may be factorycalibrated and need not require further calibrating. In certainembodiments, calibration may be required, but may be done without userintervention, i.e., may be automatic. In those embodiments in whichcalibration by the user is required, the calibration may be according toa predetermined schedule or may be dynamic, i.e., the time for which maybe determined by the system on a real-time basis according to variousfactors. Calibration may be accomplished using an in vitro test strip orother calibrator, e.g., a small sample test strip such as a test stripthat requires less than about 1 microliter of sample (for exampleFreeStyle® blood glucose monitoring test strips from Abbott DiabetesCare). For example, test strips that require less than about 1 nanoliterof sample may be used. In certain embodiments, a sensor may becalibrated using only one sample of body fluid per calibration event.For example, a user need only lance a body part one time to obtainsample for a calibration event (e.g., for a test strip), or may lancemore than one time within a short period of time if an insufficientvolume of sample is obtained firstly. Embodiments include obtaining andusing multiple samples of body fluid for a given calibration event,where glucose values of each sample are substantially similar. Dataobtained from a given calibration event may be used independently tocalibrate or combined with data obtained from previous calibrationevents, e.g., averaged including weighted averaged, filtered and thelike, to calibrate.

An analyte system may include an optional alarm system that, e.g., basedon information from a processor, warns the patient of a potentiallydetrimental condition of the analyte. For example, if glucose is theanalyte, an alarm system may warn a user of conditions such ashypoglycemia and/or hyperglycemia and/or impending hypoglycemia, and/orimpending hyperglycemia. An alarm system may be triggered when analytelevels reach or exceed a threshold value. An alarm system may also, oralternatively, be activated when the rate of change or acceleration ofthe rate of change in analyte level increase or decrease, reaches orexceeds a threshold rate or acceleration. For example, in the case of aglucose monitoring system, an alarm system may be activated if the rateof change in glucose concentration exceeds a threshold value which mightindicate that a hyperglycemic or hypoglycemic condition is likely tooccur. A system may also include system alarms that notify a user ofsystem information such as battery condition, calibration, sensordislodgment, sensor malfunction, etc. Alarms may be, for example,auditory and/or visual. Other sensory-stimulating alarm systems may beused including alarm systems which heat, cool, vibrate, or produce amild electrical shock when activated.

The subject disclosure also includes sensors used in sensor-based drugdelivery systems. The system may provide a drug to counteract the highor low level of the analyte in response to the signals from one or moresensors. Alternatively, the system may monitor the drug concentration toensure that the drug remains within a desired therapeutic range. Thedrug delivery system may include one or more (e.g., two or more)sensors, a transmitter, a receiver/display unit, and a drugadministration system. In some cases, some or all components may beintegrated in a single unit. The sensor-based drug delivery system mayuse data from the one or more sensors to provide necessary input for acontrol algorithm/mechanism to adjust the administration of drugs, e.g.,automatically or semi-automatically. As an example, a glucose sensorcould be used to control and adjust the administration of insulin froman external or implanted insulin pump.

As discussed in further detail below, in accordance with aspects of thepresent disclosure an offset based model for improving the accuracy ofthe analyte sensor signals to address abnormal sensor sensitivity eventincluding, for example, early signal (sensitivity) attenuation isprovided. More specifically, in accordance with aspects of the presentdisclosure, when an analyte sensor is experiencing signal attenuation,it is assumed that the associated sensor sensitivity remains constant,and rather, a predetermined signal offset results. Accordingly,determination of the offset and applying the determined offset toanalyte sensor signals provide improved accuracy in the monitoredanalyte levels from the sensor, even in the case where the analytesensor is experiencing an abnormal sensitivity event such as, forexample, early signal attenuation.

In this manner, in one aspect, there is provided a procedure toretrospectively (or in real time) determine a glucose estimate based onsensor signals and available reference measurements from, for example,in vitro testing that maximizes both the optimal accuracy and precision,while minimally susceptible to errors that may be caused by outlierreference value and/or momentary sensor signal degradation sources suchas early signal attenuation.

In providing the best glucose estimate, it is found that the simplesttransformation from a raw sensor current signal in arbitrary hardwareunits to a glucose signal in proper glucose concentration units is infact a linear scaling operation without any offset. The scaling factoris commonly called sensitivity, in which a raw sensor current signal canbe translated into glucose concentration units by dividing the signal'svalue by the sensitivity value. As a result, the nominal aspect ofcalibration involves using a reasonable amount of information to inferthe most accurate and precise estimate of sensitivity.

While this is the case under normal operating conditions, there areseveral exceptions in which the sensor response to the analyte may becontaminated by other artifacts. An example is during the presence ofearly signal attenuation (ESA) condition, where, suppose the sensor hasbeen properly calibrated using its true sensitivity, the resultingglucose values are lower than that determined by other means or anidentical sensor that is not subject to ESA condition.

In such non-normal operating conditions, the best glucose estimate fromthe sensor may be determined by retaining the same best estimate ofsensitivity as in the nominal case, and in addition, determining thebest estimate of a slowly time varying offset. This may be defined asthe offset based model. In one aspect, the offset based model assumesthat the true gain or sensitivity of the system remains the samethroughout the sensor's life, and that non-normal operating conditionssuch as ESA condition is best represented by a nonzero, slowly timevarying offset.

Referring now to the Figures, FIG. 6 is a flowchart illustrating theoffset model based analyte sensor data calibration in accordance withone aspect of the present disclosure. In one aspect, sampled data froman analyte sensor is processed (610). For example, in one embodiment,the raw signal (such as raw current signal) received from the analytesensor is retrospectively lag corrected, and/or filtered or smoothed inaddition to temperature corrected or compensated. Thereafter, a normalsensitivity associated with the analyte sensor is determined (620) asdescribed in further detail below in conjunction with FIG. 7.

Referring again to FIG. 6, a windowed offset is thereafter estimatedusing each available paired points (of sensor data and timecorresponding reference blood glucose measurement, for example) within avalid window (630). As discussed above, the effective sensitivityassociated with the analyte sensor is, in one embodiment, held constantat the determined normal sensitivity discussed above. Using this normalsensitivity Sn, each pairable sensor signal is scaled into glucoseconcentrations. For purpose of signal pairing, the sensor signal Gr caneither be the raw signal itself, lag corrected, and/or filtered orsmoothed in addition to temperature corrected or compensated. The scaledvalue Gs at any time k is then described as follows:Gs(k)=Gr(k)/Snwhere Sn is the normal sensitivity previously computed. For eachpairable scaled sensor signal Gs and reference blood glucose measurement(BG) in a window, a difference (DGs) may be determined as follows:DGs(k)=Gs(k)−BG(k)where k denotes the time index of a pair.

The difference value (DGs) between each pairable scaled sensor signal Gsto its corresponding reference blood glucose (BG) measurement pair is areflection of the latest offset. In one aspect, the plurality of thesecomputed offset within this window of reference blood glucosemeasurement—sensor pairs determine the windowed offset in this window.An example of obtaining a windowed offset using the available data in awindow is averaging the difference value (DGs). Yet another example isto take the median value of the difference value (DGs). Under normaloperating conditions, the windowed offset is zero.

Referring still again to FIG. 6, thereafter, the estimated offset may beused to obtain a slowly time varying offset Go(k) at every minute k(640). For a retrospective application, interpolating offset valuesobtained around clusters of reference blood glucose measurement—sensorpairs may be used in place of a slowly time varying offset Go(k). For areal-time application, prior knowledge of how offsets change over timegiven other known circumstances or parameters may be used to determine aslowly time varying offset Go(k). For example, when ESA condition issuspected, a time evolution of the offset based on the available offsetdata may be inferred by fitting the offset data to an ESA offset modelwhose architecture may have been determined a priori.

Finally, for each one minute sampled analyte data (or any otherperiodically sampled analyte data from the sensor), the slowly timevarying estimated offset Go(k) and the best estimate of the constant Snare applied to the sampled analyte data to estimate the correspondingglucose value (650). That is, for example, for each of the one minutesampled analyte sensor data Gr(k) which has been lag corrected,temperature compensated, and/or smoothed or filtered, the slowly timevarying offset Go(k) is applied with the previously determined normalsensitivity Sn to determine the corresponding estimated glucose valueGf(k) based on the following relationship:Gf(k)=[Gr(k)/Sn]+Go(k)

FIG. 7 is a flowchart illustrating the normal sensitivity Sndetermination routine of FIG. 6 associated with the analyte sensor inaccordance with one embodiment of the present disclosure. Referring tothe Figure, to determine the normal sensitivity associated with theanalyte sensor (620) (FIG. 6), given the available paired data points ofanalyte sensor signals and the time corresponding reference measurements(for example, the in vitro blood glucose measurements taken, forexample, at given time intervals (periodic or otherwise)), the immediatesensitivity (Si) for each paired data points are determined (710). Thatis, in one embodiment, for each paired sensor data and reference bloodglucose measurement, the sensor data is lag corrected and smoothed orfiltered, based on a nominal time constant (for example, 10 minutes orany other suitable time period), and a ratio of the lag corrected andsmoothed data Gr(k) over the reference blood glucose measurement BG istaken to determine the corresponding immediate sensitivity (Si).Si(k)=Gr(k)/BG

Referring back to FIG. 7, with the determined immediate sensitivity(Si), for each time window (which may be preset or variable), the latestsensitivity (S0) associated with the analyte sensor is estimated basedon the determined immediate sensitivity (Si) values within that timewindow (720). That is, a time window such as, for example, a five hourwindow (or any other suitable time window) is defined with a center thatshifts or advances through the sensor's life (as a measure of time) inan increment of, for example, one hour. Thereafter, within the timewindow, the determined immediate sensitivity (Si) is collected as wellas the associated rate of change of the sensor signals.

In one embodiment, a least squares fit line is calculated for the sensorrate of change as a function of the corresponding immediate sensitivity(Si) within the time window. The vertical difference between eachimmediate sensitivity (Si) and the calculated least squares fit linecorresponds to a lag residual compensated sensitivity. Furthermore, theintercept at the zero rate of change of this least squares fit linecorresponds to the estimate of the latest sensitivity (S0) in thecorresponding time window.

It is to be noted that the standard error associated with the leastsquares fit line may correspond to how the available data fits the lagcorrection model as well as how much variance is introduced from thezero mean sensitivity error sources. In this context, the zero meansensitivity error sources are factors that could increase the varianceof the sensitivity calculation error without significantly biasing theresult in any direction. Examples of such zero mean sensitivity errorsources include random sensor error and/or noise, random reference bloodglucose error, and insufficient lag correction. Insufficient lagcorrection may result from using a time constant that is smaller orlarger than the actual value, or from using a model that is notsufficiently robust to capture all the transient behavior between bloodglucose levels to interstitial glucose levels.

Also, referring back to FIG. 7, using the available lag residualcompensated sensitivity values, along with their associated timestamps,in one embodiment, the least squares fit line may be determined based ontime as a function of the lag residual compensated sensitivity values toestimate the sensitivity rate of change for each time window. In thismanner, as discussed above, for each time window, the latest sensitivity(S0) based on the determined immediate sensitivity (Si) is calculated.Referring still to FIG. 7, given the multiple time windows, a subset ofthe time windows are selected based on the determined latestsensitivity, the immediate sensitivity (Si) as well as the lag residualcompensated sensitivity values. In this manner, in one aspect, thelatest sensitivity values (S0) that are taken during non-normaloperation modes such as during ESA condition are discounted. In thesecases, the latest sensitivity values (S0) tend to be lower than thetrue/accurate value. For example, in one embodiment, the time windowsare selected for latest sensitivity values (S0) that are in the upper50^(th) percentile of the entire time window population.

Also, the subset of time windows are additionally identified for thosewith a suitable or sufficient least squares fit line of the sensor rateof change versus immediate sensitivity (Si). The better the fit, themore likely a given window will produce a reliable latest sensitivityvalue (S0). In one embodiment, the latest sensitivity values (S0)retained are those where the standard sensitivity error (Sse) based onthe determined least squares fit line in the lower quartile of theentire time window population.

Additionally, the subset of time windows may further be narrowed tothose associated with a relatively low immediate sensitivity (Si) rateof change value. A window with a relatively high immediate sensitivity(Si) rate of change value may indicate a region of poor sensorstability, or a consistent bias in the reference blood glucose valuesdue to unknown circumstances. For example, in one embodiment, onlylatest sensitivity values (S0) whose rate of change magnitude is in thelower quartile of the entire time window population may be retained.Referring still to FIG. 7, based on the one or more criteria describedabove, the subset of eligible latest sensitivities (S0) are filtered oridentified from all latest sensitivity values (S0) (730). It is to benoted that the threshold for inclusion within the subset of time windowsmay be varied and include other thresholds or criteria including, forexample, selecting those time windows associated with the latestsensitivity in the upper 75^(th) percentile of the entire population (orsome other suitable threshold), selecting those time windows associatedwith preferred elapsed time ranges since the start of a sensorinsertion, or selecting those time windows associated with preferredranges of times of days. Indeed, the numerical examples described hereinare intended to provide exemplary embodiments and the scope of thepresent disclosure is not in any manner intended to be limited to suchexamples.

Referring back to FIG. 7, as shown, weighted averaging function isapplied to the subset of eligible latest sensitivity values (S0) todetermine the estimate of the normal sensitivity (740). For example, inone embodiment, each latest sensitivity value (S0) may be weighted by(1/Sse)². In another embodiment, each latest sensitivity value (S0) maybe weighted by (S0/(Sse)²). In yet another embodiment, other measures offit such as the absolute value of immediate sensitivity (Si) rate ofchange of each window can be included into the weighting.

Thereafter, the estimated normal sensitivity determined is confirmed by,for example, comparing it to the median sensitivity computed from alleligible latest sensitivity values (S0) and ensuring that the estimatednormal sensitivity is no smaller than the median sensitivity (750).Since numerical determination may incur a certain degree of uncertainty,it is possible that the normal sensitivity candidate may be lower thansome clusters of latest sensitivity values (S0) that may be a bettercandidate for the normal sensitivity estimate. As long as the bottom endof the uncertainty of the latest sensitivity values (S0) is still belowthe candidate normal sensitivity value, no adjustment may be needed.Otherwise, the normal sensitivity may be adjusted further up to thatbottom end limit. An example of computing the bottom end of latestsensitivity values (S0) may include subtracting each latest sensitivityvalue (S0) with three times (or any other suitable factor) thecorresponding standard error (Sse) value. When the mean of this lowerbound is higher than the candidate normal sensitivity value, the normalsensitivity should be adjusted to this bound.

In this manner, in accordance with embodiments of the presentdisclosure, improved real time or retrospective determination of glucoseestimate is provided based on analyte sensor data and associated timecorresponding reference measurement values (for example, in vitro testresults providing associated blood glucose measurements) which improvesaccuracy and is less prone to abnormal sensor sensitivity events suchas, for example, early signal attenuation.

Furthermore, in aspects of the present disclosure, it is contemplatedthat the highest sustainable in vivo steady state sensitivity associatedwith an analyte sensor occurs when the sensor is in normal condition.Also, sensitivities determined during reduced or increased meansensitivity events may be deemed poor or inaccurate representatives ofnormal sensitivity. Additionally, zero mean sensitivity error sourcesare not considered to bias the normal sensitivity, and further, lagcorrection (retrospective or real time) of the analyte sensor raw signalremoves most of the rate of change associated sensitivity errors.Moreover, it is considered, in some aspects of the present disclosure,that within a relatively short time window, a single “latestsensitivity” is an accurate representation of the temporalsensitivity—for example, in a five hour time window as in the exemplarydiscussion set forth above, the “latest sensitivity” may be consideredsufficiently representative.

In addition, in a five hour time window, a single effective timeconstant may be applicable for all available paired points. Whiledifferent time windows may have different associated time constants, ina steady state condition where other parameters or variables are thesame, the time window that has an effective time constant which isclosed to an assumed nominal value may have a latest sensitivity valuethat is more suitable for the normal sensitivity. Finally, in a timewindow with sufficient number of paired points, the determinedsensitivity slope over time may indicate the relative stability of thesensitivity in that time window. As such, again assuming a steady statecondition where other parameters or variables are considered to be thesame, a time window with a flatter sensitivity slope over time may be amore suitable candidate for the normal sensitivity.

Accordingly, a method in one embodiment includes processing sampled datafrom analyte sensor, determining a single, fixed, normal sensitivityvalue associated with the analyte sensor, estimating a windowed offsetvalue associated with the analyte sensor for each available sampled datacluster, computing a time varying offset based on the estimated windowedoffset value, and applying the time varying offset and the determinednormal sensitivity value to the processed sampled data to estimate ananalyte level for the sensor.

Processing the sampled data may include performing retrospective lagcorrection of the sampled data. Further, processing the sampled data mayinclude smoothing the sampled data from the analyte sensor. In addition,processing the sampled data may include performing temperaturecorrection to the sampled data.

In one aspect, determining the normal sensitivity may include pairingthe sampled data from the analyte sensor with one or more timecorresponding reference measurement values, where the one or morereference measurement values may include a blood glucose measurement.

A further aspect may include determining an immediate sensitivity valuefor each paired sampled data and the one or more time correspondingreference measurement values, and also, estimating a latest sensitivitybased on the determined immediate sensitivity for each time window.

Yet a further aspect may include defining a subset of the estimatedlatest sensitivities associated with a subset of the total availabletime windows corresponding to the respective paired sampled data and theone or more time corresponding reference measurement values.

Additionally, still a further aspect may include weighted averaging thesubset of estimated latest sensitivities to determine the normalsensitivity associated with the analyte sensor.

Also, another aspect may include confirming the determined normalsensitivity, where confirming the determined normal sensitivity mayinclude comparing the determined normal sensitivity to a predeterminedvalue, and further, where predetermined value may include a mediansensitivity value determined based on the immediate sensitivityassociated with each time window.

Also, estimating a windowed offset value in an eligible cluster of datamay include collecting one or more pairs of reference measurement valueand normal sensitivity adjusted sensor signal to determine the offset ofeach pair.

The windowed offset value of each pair in a window may be collected todetermine a windowed offset value that is most representative of thatwindow.

Additionally, determining a windowed offset value that is mostrepresentative of that window may include taking the median of theoffset values of each pair in a window, taking the mean of the offsetvalues of each pair in a window, taking a weighted mean of the offsetvalues of each pair in a window, or other means of estimating the mostrepresentative offset value given the population of offset values in awindow.

Moreover, slowly time varying offset may be determined based on anyavailable windowed offset values using simple interpolation betweenwindowed offset values.

In addition, a slowly time varying offset may be determined by fitting apredetermined mathematical model using any available windowed offsetvalues. One example is a mathematical model similar to the impulseresponse of a second order model, with the time constants, amplitude,and the start of the response determined by fitting any availablewindowed offset values.

The estimate of an analyte level for the sensor may be obtained bydividing the latest unscaled value by the normal sensitivity, and thensubtracting the result with the latest slowly time varying offset.

An apparatus in accordance with another aspect of the present disclosureincludes a data communication interface, one or more processorsoperatively coupled to the data communication interface and a memory forstoring instructions which, when executed by the one or more processors,causes the one or more processors to process sampled data from analytesensor, determine a single, fixed, normal sensitivity value associatedwith the analyte sensor, estimate a windowed offset value associatedwith the analyte sensor for each available sampled data cluster, computea time varying offset based on the estimated windowed offset value, andapply the time varying offset and the determined normal sensitivityvalue to the processed sampled data to estimate an analyte level for thesensor.

One or more storage devices having processor readable code embodiedthereon, said processor readable code for programming one or moreprocessors to estimate analyte level in accordance with a further aspectof the present disclosure includes processing sampled data from analytesensor, determining a single, fixed, normal sensitivity value associatedwith the analyte sensor, estimating a windowed offset value associatedwith the analyte sensor for each available sampled data cluster,computing a time varying offset based on the estimated windowed offsetvalue, and applying the time varying offset and the determined normalsensitivity value to the processed sampled data to estimate an analytelevel for the sensor.

The various processes described above including the processes performedby the data processing unit 102, receiver unit 104/106 or the dataprocessing terminal/infusion section 105 (FIG. 1) in the softwareapplication execution environment in the analyte monitoring system 100including the processes and routines described in conjunction with FIGS.6-7, may be embodied as computer programs developed using an objectoriented language that allows the modeling of complex systems withmodular objects to create abstractions that are representative of realworld, physical objects and their interrelationships. The softwarerequired to carry out the inventive process, which may be stored in thememory or storage device (not shown) of the data processing unit 102,receiver unit 104/106 or the data processing terminal/infusion section105, may be developed by a person of ordinary skill in the art and mayinclude one or more computer program products.

Various other modifications and alterations in the structure and methodof operation of this disclosure will be apparent to those skilled in theart without departing from the scope and spirit of the embodiments ofthe present disclosure. Although the present disclosure has beendescribed in connection with particular embodiments, it should beunderstood that the present disclosure as claimed should not be undulylimited to such particular embodiments. It is intended that thefollowing claims define the scope of the present disclosure and thatstructures and methods within the scope of these claims and theirequivalents be covered thereby.

What is claimed is:
 1. A method, comprising: positioning a first portionof an analyte sensor in fluid contact with bodily fluid under a skinsurface; electrically coupling a second portion of the analyte sensorwith a power source to provide power to the analyte sensor while thefirst portion of the analyte sensor is positioned in fluid contact withthe bodily fluid under the skin surface; generating, with the analytesensor, signals corresponding to an analyte level in the bodily fluid;generating a sampled data based on the signals from the analyte sensor;determining that the sampled data is associated with an abnormal sensorsensitivity event; estimating an offset value based on the sampled data;determining a time varying offset based on the estimated offset value;estimating the analyte level based on the determined time varyingoffset; and providing a predictive alarm to a user based on theestimated analyte level.
 2. The method of claim 1, further includingperforming retrospective lag correction of the sampled data.
 3. Themethod of claim 1, further including smoothing the sampled data.
 4. Themethod of claim 1, further including performing temperature correctionfor the sampled data.
 5. The method of claim 1, further includingdetermining a sensitivity value associated with the analyte sensor. 6.The method of claim 5, wherein determining the sensitivity valueincludes pairing the sampled data with one or more time correspondingreference measurement values.
 7. The method of claim 5, furtherincluding applying the sensitivity value to the sampled data.
 8. Anapparatus, comprising: an analyte sensor having a first portion and asecond portion, the first portion positioned in fluid contact with abodily fluid under a skin surface, the analyte sensor configured togenerate signals corresponding to an analyte level in the bodily fluid;a power source electrically coupled to the second portion of the analytesensor to provide power to the analyte sensor for generating the signalswhile the first portion of the analyte sensor is in fluid contact withthe bodily fluid under the skin surface; a data communication interface;one or more processors operatively coupled to the data communicationinterface and the analyte sensor; and a memory operatively coupled tothe one or more processors for storing instructions which, when executedby the one or more processors, causes the one or more processors togenerate a sampled data based on the signals from the analyte sensor,and to determine that the sampled data is associated with an abnormalsensor sensitivity event, to estimate an offset value based on thesampled data, to determine a time varying offset based on the estimatedoffset value, to estimate the analyte level based on the determined timevarying offset, and provide a predictive alarm to a user based on theestimated analyte level.
 9. The apparatus of claim 8, wherein the memoryfor storing instructions, when executed by the one or more processors,causes the one or more processors to perform retrospective lagcorrection of the sampled data.
 10. The apparatus of claim 8, whereinthe memory for storing instructions, when executed by the one or moreprocessors, causes the one or more processors to smooth the sampleddata.
 11. The apparatus of claim 8, wherein the memory for storinginstructions, when executed by the one or more processors, causes theone or more processors to perform temperature correction for the sampleddata.
 12. The apparatus of claim 8, wherein the memory for storinginstructions, when executed by the one or more processors, causes theone or more processors to determine a sensitivity value associated withthe analyte sensor.
 13. The apparatus of claim 12, wherein the memoryfor storing instructions, when executed by the one or more processors,causes the one or more processors to pair the sampled data with one ormore time corresponding reference measurement values.
 14. The apparatusof claim 12, wherein the memory for storing instructions, when executedby the one or more processors, causes the one or more processors toapply the sensitivity value to the sampled data.
 15. The method of claim1, further including outputting on a display device informationassociated with the estimated analyte level.
 16. The method of claim 15,wherein the information associated with the estimated analyte levelincludes one or more of a visual or an audible representationcorresponding to the estimated analyte level, a rate of change of theestimated analyte level, and a direction of the rate of change of theestimated analyte level.
 17. The method of claim 1, wherein the abnormalsensor sensitivity event includes an early signal attenuation event. 18.The apparatus of claim 8, wherein the data communication interfacewirelessly communicates the estimated analyte level to a receiver havinga display to output information associated with the estimated analytelevel on the display.
 19. The apparatus of claim 18, wherein theinformation associated with the estimated analyte level includes one ormore of a visual or an audible representation corresponding to theestimated analyte level, a rate of change of the estimated analytelevel, and a direction of the rate of change of the estimated analytelevel.
 20. The apparatus of claim 8, wherein the abnormal sensorsensitivity event includes an early signal attenuation event.